Comparison of predictive equations for resting energy expenditure in overweight and obese adults

Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults

BACKGROUND

Although over one hundred equations have been developed to predict the energy expenditure of individuals, none are sensitive to weight change in assessment of resting metabolic rate (RMR) before and after weight loss.

OBJECTIVE

To formulate adjusted equations for overweight and obese individuals and to compare their accuracy with existing prediction RMR equations before and after weight loss.

SUBJECTS/MATERIALS

This is historical prospective study. Participants included 39 overweight and obese men and women before and after losing 10-20% from baseline weight on a diet and physical activity regimen for at least three months. Pre and post weight loss measured RMR results were compared to estimated RMR using several existing prediction equations: Harris and Benedict, Ravussin and Bogardus, and Mifflin et al. To improve the accuracy of these prediction equations, we suggest new equations adjusted for weight loss, based on measured RMR and evaluated their accuracy.

RESULTS

Pre and post weight loss data indicated: significant fat reduction in both genders; reduction in free-fat mass only in men, and a significant decrease in measured RMR only in women. Our suggested equations were the most accurate and closest to measured RMR in both genders, in comparison to the Harris and Benedict, Ravussin and Bogardus, and Mifflin et al equation results. Estimated RMR using the latter equations was significantly lower than measured RMR in both genders at pre and post weight loss (P < 0.01).

CONCLUSIONS

This study highlights the need for adjusting RMR equations before and after weight loss in overweight and obese individuals. Further research is needed to validate our suggested equations.

New Predictive Equations for Resting Energy Expenditure in Normal to Overweight and Obese Population

BACKGROUND AND AIMS

The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population.

METHODS

A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18-57 ± 14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men = 50%).

RESULTS

Using a number of parameters (bias, underprediction, overprediction, and % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (∼55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as the Almajwal-Abulmeaty (AA) equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE = 3832.955 + AdjWt (kg) × 48.037 - Ht (cm) × 30.642 + gender × 141.268 - age (years) × 4.525 [AdjWt is Adjusted body weight = (Wt - IBW)/4 + IBW. IBW is Ideal body weight; for men IBW = (Ht(cm) - 152.4) × 1.0714) + 45.36 and for women IBW = (Ht(cm)-152.4) × 0.8928) + 45.36]. The regression model accounted for approximately 70% of the variance in REEm (R 2 = 0.702).

CONCLUSION

Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations.

Predicting Equations and Resting Energy Expenditure Changes in Overweight Adults

Introduction

The aim of the study is to show the differences between the measured and estimated values of resting energy expenditure and any changes occurring after the 6-month weight loss intervention program.

Methods

We included 33 healthy adults aged 25–49 years with an average body mass index 29.1±2.7 kg/m 2 for female and 29.8±2.8 kg/m² for male. The measured resting energy expenditure was obtained by indirect calorimeter MedGem® Microlife and estimated resting energy expenditure by the Harris–Benedict equation, the Mifflin–St Jeor equation, the Owen equation, the Wright equation, and by the Tanita body composition analyser. All measurements and calculations were carried out before and after the 6-month intervention. Results were compared using paired t-tests. P value less than 0.05 was considered statistically significant.

Results

A comparison of the measured resting energy expenditure of female subjects with the estimated resting energy expenditure using the Harris–Benedict equation, the Mifflin–St Jeor equation and the Wright equation showed a statistically significant difference. A comparison of the measured resting energy expenditure of male subjects with the estimated resting energy expenditure using the Harris–Benedict equation and the Wright equation showed a statistically significant difference. There was a significant difference in the measured resting energy expenditure and estimated resting energy expenditure using Tanita.

Conclusions

We concluded that the most comparable equation for our sample was the Owen’s equation. After losing weight, the measured resting energy expenditure has decreased, which must be taken into account in further diet therapy.

Moderate Increase in Protein Intake Promotes a Small Additional Improvement in Functional Capacity, But Not in Muscle Strength and Lean Mass Quality, in Postmenopausal Women Following Resistance Exercise: A Randomized Clinical Trial

The aim of this study was to evaluate the effect of a moderate increase in protein intake on muscle strength, functional capacity and lean mass quality improvements in postmenopausal women following resistance exercise. Forty-seven postmenopausal women were randomized in two groups: Normal protein (NP, n = 25), who received a dietary plan containing ~0.8 g protein·kg^-1·d^-1 (recommended dietary allowance-RDA recommendations); and higher protein (HP, n = 22), which a moderate increase in protein intake was recommended (~1.2 g protein·kg^-1·d^-1). Resistance training was performed for 10 weeks, three times/week. Muscle strength (handgrip strength and one repetition maximum test-1-RM), functional capacity and lean mass (LM) quality (muscle strength to lean mass ratio) were evaluated. Dietary intake was assessed by nine 24 h food recalls. After intervention, both groups increased similarly the leg extension 1-RM and handgrip strength. Regarding functional capacity tests, both groups increased the balance test score (SPPB) and 10 m walk test speed, with no differences between the groups. In addition, an increase in speed to perform the 6 min and 400 m walk tests was observed over the time, with an additional improvement in the HP group (time × group interaction; p = 0.007 and p = 0.004, respectively). About LM quality, leg extension 1-RM/leg LM improved over the time in both groups (p = 0.050), with no time × group interaction. All these significant changes had a low effect size. In conclusion, a moderate increase in protein intake promoted a small additional improvement in functional capacity, but it did not induce a greater increase in strength and LM quality after 10 weeks of resistance exercise in postmenopausal women. This trial was registered at ClinicalTrials.gov as NCT03024125.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes

Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875-1881, 2018-The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.

Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children

BACKGROUND

To study the predictive factors of resting energy expenditure (REE) and evaluate the accuracy of predicted equations with indirect calorimeter (IC) in Chinese school-age children, particularly for the obese population.

METHODS

Recruited children were from the department of child healthcare in Nanjing children's hospital during July 2014-September 2015. Anthropometric parameters and body composition were measured by bioelectrical impedance. Measured REE was assessed by IC. Predicted REE was estimated using ten published equations.

RESULTS

248 children aged 7-13 years were recruited, including 148 obese [body mass index standard deviation score (BMISDS) = 2.48 ± 0.91] and 100 non-obese (BMISDS = - 0.96 ± 1.08). The unit mass of REE (REE/kg) in obese group (29.06 ± 5.74) was lower than that in non-obese group (37.51 ± 6.56). The stepwise regression showed that age, BMISDS and fat-free mass (FFM) had a major impact on REE/kg as the regression equation: Y = 54.41 - 1.36 × X₁ - 2.25 × X₂ - 0.16 × X₃ (Y REE/kg, X₁ age, X₂ BMISDS, X₃ FFM; R = 0.633, R² = 0.401, P < 0.01). The accuracy of predicted REE in obese subjects was 62.16% by the new predictive equations.

CONCLUSIONS

The REE/kg in obese children was lower and closely correlated with age, BMISDS and FFM. It is necessary to validate the new predictive equation in a larger sample to estimate energy requirements, particularly for children with obesity.

Higher Protein Intake Does Not Improve Lean Mass Gain When Compared with RDA Recommendation in Postmenopausal Women Following Resistance Exercise Protocol: A Randomized Clinical Trial

The aim of this study was to evaluate the effect of a higher protein intake on lean body mass (LBM) gain in postmenopausal women practicing resistance exercise and compare it to the Recommended Dietary Allowance (RDA) recommendation. Twenty-three postmenopausal women (63.2 ± 7.8 years) were randomized into two groups. The group with higher protein intake (n = 11) (HP) received a dietary plan with ~1.2 g·kg^-1·day^-1 of protein, while the normal protein (NP) group (n = 12) was instructed to ingest ~0.8 g·kg^-1·day^-1 of protein (RDA recommendation). Both groups performed the same resistance training protocol, 3 times a week, with progression of the number of sets (from 1 to 6 sets) and 8-12 repetitions. The intervention occurred over 10 weeks. Body composition evaluation was performed by dual-energy X-ray absorptiometry. The diet was evaluated by nine 24-h food recall summaries over the course of the study. During the intervention period, the HP group presented a higher protein (1.18 ± 0.3 vs. 0.87 ± 0.2 g·kg^-1·day^-1, p = 0.008) and leucine (6.0 ± 1.4 vs. 4.3 ± 0.9 g/day, p < 0.001) intake than the NP group, respectively. At the end of the intervention, there were increases in LBM both in HP (37.1 ± 6.2 to 38.4 ± 6.5 kg, p = 0.004) and in NP (37.6 ± 6.2 to 38.8 ± 6.4 kg, p < 0.001), with no differences between the groups (p = 0.572). In conclusion, increased protein intake did not promote higher LBM gain when compared to RDA recommendation in postmenopausal women performing resistance exercise during 10 weeks. This trial was registered at ClinicalTrials.gov as NCT03024125.

Estimation of basal metabolic rate in Chinese: are the current prediction equations applicable?

BACKGROUND

Measurement of basal metabolic rate (BMR) is suggested as a tool to estimate energy requirements. Therefore, BMR prediction equations have been developed in multiple populations because indirect calorimetry is not always feasible. However, there is a paucity of data on BMR measured in overweight and obese adults living in Asia and equations developed for this group of interest. The aim of this study was to develop a new BMR prediction equation for Chinese adults applicable for a large BMI range and compare it with commonly used prediction equations.

METHODS

Subjects were 121 men and 111 women (age: 21-67 years, BMI: 16-41 kg/m(2)). Height, weight, and BMR were measured. Continuous open-circuit indirect calorimetry using a ventilated hood system for 30 min was used to measure BMR. A regression equation was derived using stepwise regression and accuracy was compared to 6 existing equations (Harris-Benedict, Henry, Liu, Yang, Owen and Mifflin). Additionally, the newly derived equation was cross-validated in a separate group of 70 Chinese subjects (26 men and 44 women, age: 21-69 years, BMI: 17-39 kg/m(2)).

RESULTS

The equation developed from our data was: BMR (kJ/d) = 52.6 x weight (kg) + 828 x gender + 1960 (women = 0, men = 1; R(2) = 0.81). The accuracy rate (within 10 % accurate) was 78 % which compared well to Owen (70 %), Henry (67 %), Mifflin (67 %), Liu (58 %), Harris-Benedict (45 %) and Yang (37 %) for the whole range of BMI. For a BMI greater than 23, the Singapore equation reached an accuracy rate of 76 %. Cross-validation proved an accuracy rate of 80 %.

CONCLUSIONS

To date, the newly developed Singapore equation is the most accurate BMR prediction equation in Chinese and is applicable for use in a large BMI range including those overweight and obese.

Resting Energy Expenditure of Black Overweight Women in South Africa Is Lower than of White Women

BACKGROUND

Overweight affects 65% of black South African women. Effective weight management requires accurate measurement or estimation of energy expenditure.

AIMS

The study aimed to determine, among overweight women, whether measured resting energy expenditure (REE) differs between black and white participants, and the performance of REE estimation equations.

METHODS

The REE of 44 black (age 39.6 ± 9.7 years, body mass index (BMI) 35.1 ± 6.2 kg/m2) and 41 white (age 38.0 ± 11.6 years, BMI 33.9 ± 7.6 kg/m2) women was measured with indirect calorimetry and estimated with equations. Body composition was assessed with multi-frequency bioelectrical impedance analysis. Differences in REE were determined with t tests (Welch), and included adjustment for fat free mass (FFM) and BMI, and for FFM index (FFMI).

RESULTS

Measured REE was 585 kJ/day (95% CI 264-905; p = 0.0005) and 861 kJ/day (95% CI 499-1,221; p < 0.0001) lower in black than in white women when adjusted for FFM and BMI, and FFMI, respectively. Out of 14 equations, 13 underestimated REE (error range 2,261 ± 727 kJ/day (Bernstein equation, white women) to 8 ± 782 kJ/day (BMI equation, black women)).

CONCLUSIONS

Black overweight women have significantly lower REE than their white counterparts. No tested estimation equation provided satisfactory results across race/ethnicity. REE measurements or development of overweight- or race/ethnicity-specific estimation equations are recommended.

Resting energy expenditure (REE) in an old-old population: implications for metabolic stress

The basis of nutritional therapy and thus an adequate nutrient intake is the assessment of energy need. On the other end, the assessment of individual energy requirements based on the gold standard, indirect calorimetry, is associated with feasibility difficulties in geriatric settings. To identify the most accurate predictive equations for resting energy expenditure (REE) in older subjects with overweight, 17 predictive equations were compared to indirect calorimetry measurement in a study population of 20 obese older subjects (mean BMI 33.7±4.5kg/m(2); mean age 79.8±8.1 years; gender 5 males and 15 females) and 20 age-matched controls with a normal body weight (mean BMI 24.9±2.5 kg/m(2); mean age 82.1±6.6 years; gender 9 males and 11 females). The comparison led to two significant observations: the predictive equations used led to a much better estimation of the REE in the control group than in the obese older subjects. In addition, the most accurate equation for estimating the REE in the obese older subjects has been shown to be that by Lührmann et al. Further studies are needed to assess the feasibility of using this equation in a routine geriatric setting.

Predicted vs. Actual Resting Energy Expenditure and Activity Coefficients: Post-Gastric Bypass, Lean and Obese Women

Total Energy Expenditure (TEE) and energy requirements are commonly estimated from equations predicting Resting Energy Expenditure (REE) multiplied by a Physical Activity (PA) coefficient that accounts for both PA energy expenditure and the thermogenic effect of food. PA coefficients based on PA self-reports are a potential source of error that has not been evaluated. Therefore, in this study we compared: 1) the Harris-Benedict (HB), Mifflin-St. Jeor (MSJ), and the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) REE equations with REE measured (REE-m) with indirect calorimetry; 2) PA coefficients determined with PA self-reports vs. objectively assessed PA; and 3) TEE estimates in post-Gastric Bypass (GB = 13), lean (LE = 7), and obese (OB = 12) women. REE was measured in the morning after an overnight fast with participants resting supine for 30 min. Self-reported PA was evaluated with a questionnaire and objectively measured with accelerometers worn for 5-7 days. Nutritional intake was evaluated with a food frequency questionnaire. Anthropometry included DEXA, and abdominal CT scans. Eligible GB had surgery ≥ 12 months before the study, and had ≥ 10 kg of body weight loss. All participants were 18-45 years of age, able to engage in ambulatory activities, and not taking part in exercise training programs. One-way ANOVA was used to detect differences in REE and TEE. Accuracy of REE prediction equations were determined by cases within 10% of REE-m, and agreement analyses. REE predictions were not different than REE-m, but agreements were better with HB and MSJ, particularly in the GB and LE groups. Discrepancies in the PA coefficients determined with self-report vs. objectively assessed PA resulted in TEE overestimates (approximately 200-300 Kcal/day) using HB and MSJ equations. FAO/WHO/UNU overestimated TEE in all groups regardless of the PA assessment method (approximately 300-900 kcal/day). These results suggest that: 1) HB and MSJ equations are good predictors of REE among GB and LE, but not among OB women, 2) PA coefficients used to estimate TEE must be determined with objective PA assessment, and 3) TEE estimates using PA coefficients with the FAO/WHO/UNU equation must be used with caution.

Measuring energy expenditure in clinical populations: rewards and challenges

The measurement of energy expenditure (EE) is recommended as an important component of comprehensive clinical nutrition assessments in patients with altered metabolic states, who failed to respond to nutrition support and with critical illness that require individualized nutrition support. There is evidence that EE is variable in patients with metabolic diseases, such as chronic renal disease, cirrhosis, HIV, cancer cachexia, cystic fibrosis and patients under intensive care. By using appropriate techniques and interpretations of basal or resting EE, clinicians can facilitate the adequate nutrition support with minimum negative impacts from under- or overfeeding in these patients. This review is based on our current understanding of the different components of EE and the techniques to measure them, and to re-examine advances and challenges to determine energy needs in clinical populations with more focuses on the obese, pediatric and elderly patients. In addition, technological advances have expanded the choices of market-available equipments for assessing EE, which also bring specific challenges and rewards in selecting the right equipment with specific performance criteria. Lastly, analytical considerations of interpreting the results of EE in the context of changing body composition are presented and discussed.

Comparison of five equations for estimating resting energy expenditure in Chinese young, normal weight healthy adults

BACKGROUND

Most resting energy expenditure (REE) predictive equations for adults were derived from research conducted in western populations; whether they can also be used in Chinese young people is still unclear. Therefore, we conducted this study to determine the best REE predictive equation in Chinese normal weight young adults.

METHODS

Forty-three (21 male, 22 female) healthy college students between the age of 18 and 25 years were recruited. REE was measured by the indirect calorimetry (IC) method. Harris-Benedict, World Health Organization (WHO), Owen, Mifflin and Liu's equations were used to predictREE (REEe). REEe that was within 10% of measured REE (REEm) was defined as accurate. Student's t test, Wilcoxon Signed Ranks Test, McNemar Test and the Bland-Altman method were used for data analysis.

RESULTS

REEm was significantly lower (P < 0.05 or P < 0.01) than REEe from equations, except for Liu's, Liu's-s, Owen, Owen-s and Mifflin in men and Liu's and Owen in women. REEe calculated by ideal body weight was significantly higher than REEe calculated by current body weight ( P < 0.01), the only exception being Harris-Benedict equation in men. Bland-Altman analysis showed that the Owen equation with current body weight generated the least bias. The biases of REEe from Owen with ideal body weight and Mifflin with both current and ideal weights were also lower.

CONCLUSIONS

Liu's, Owen, and Mifflin equations are appropriate for the prediction of REE in young Chinese adults. However, the use of ideal body weight did not increase the accuracy of REEe.

Resting energy expenditure and carbohydrate oxidation are higher in elderly patients with COPD: a case control study

BACKGROUND

Elderly patients with chronic obstructive pulmonary disease (COPD) usually have a compromised nutritional status which is an independent predictor of morbidity and mortality. To know the Resting Energy Expenditure (REE) and the substrate oxidation measurement is essential to prevent these complications. This study aimed to compare the REE, respiratory quotient (RQ) and body composition between patients with and without COPD.

METHODS

This case-control study assessed 20 patients with chronic obstructive pulmonary disease attending a pulmonary rehabilitation program. The group of subjects without COPD (control group) consisted of 20 elderly patients attending a university gym, patients of a private service and a public healthy care. Consumption of oxygen (O₂) and carbon dioxide (CO₂) was determined by indirect calorimetry and used for calculating the resting energy expenditure and respiratory quotient. Body mass index (BMI) and waist circumference (WC) were also measured. Percentage of body fat (%BF), lean mass (kg) and muscle mass (kg) were determined by bioimpedance. The fat free mass index (FFMI) and muscle mass index (MMI) were then calculated.

RESULTS

The COPD group had lower BMI than control (p = 0.02). However, WC, % BF, FFMI and MM-I did not differ between the groups. The COPD group had greater RQ (p = 0.01), REE (p = 0.009) and carbohydrate oxidation (p = 0.002).

CONCLUSIONS

Elderly patients with COPD had higher REE, RQ and carbohydrate oxidation than controls.